Evidence map›Paper›PMID 42489880›Full record

ReviewPediatric radiology2026

Current challenges for global equity related to the implementation of artificial intelligence in pediatric imaging.

Rutger A J Nievelstein, Amit Gupta, Joanna Kasznia-Brown, Nasreen Mahomed, Jaishree Naidoo, Marla B K Sammer

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Pediatric radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Rutger A J NievelsteinDepartment of Pediatric Radiology & Nuclear Medicine, University Medical Center Utrecht/Wilhelmina Children's Hospital, P.O. Box 85500, 3508 GA, Utrecht, Netherlands. r.a.j.nievelstein@umcutrecht.nl.ORCID http://orcid.org/0000-0002-0484-1486
Amit GuptaDepartment of Diagnostic and Interventional Oncoradiology, All India Institute of Medical Sciences, New Delhi, India.
Joanna Kasznia-BrownDepartment of Pediatric Radiology, Musgrove Park Hospital, University of Bristol, Bristol, UK.
Nasreen MahomedDepartment of Radiology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Jaishree NaidooPaediatric Diagnostic Imaging, Dr. J. Naidoo Inc., Johannesburg, South Africa.
Marla B K SammerEdward B. Singleton Department of Radiology, Texas Children's Hospital, Baylor College of Medicine, Houston, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is rapidly transforming medical imaging, offering unprecedented opportunities to enhance diagnostic accuracy, streamline workflows, and personalize care. However, its integration into pediatric radiology presents unique challenges that threaten to widen existing global health disparities if not addressed thoughtfully. These challenges are related to data inequality and bias in model development, infrastructure disparities, regulatory and ethical gaps, workforce capacity and training gaps, language and localization barriers, costs and commercialization, and sustainability and long-term support issues. This article, written by representatives from the World Federation of Pediatric Imaging (WFPI), will address the key barriers to global validation and implementation of AI in pediatric radiology and how they can be addressed. By linking these domains to practical actions and responsibilities and outlining a time-sequenced roadmap, this paper provides an equity-focused, pediatric-specific framework to guide global implementation.

Indexed as

Artificial IntelligenceDiagnostic ImagingGlobal HealthHealth EquityPediatricsRadiologyChildHumansArtificial intelligenceChild healthEquityGlobalImagingPediatricsRadiology

Identifiers

PMID42489880
PMCPMC13546331

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.